model {
  ## priors
  sigmaRes ~ dt(0,1,1)I(0,)
  tauRes <- pow(sigmaRes, -2)

  beta0 ~ dnorm(0, 0.01)
  beta1 ~ dnorm(0, 0.01)
  
  ## likelihood
  for(i in 1:n){
    y[i] ~ dnorm(mu[i], tauRes)
    mu[i] <- beta0 + beta1*x[i]
  }

}